• Korean Journal of Applied Biomechanics
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• v.25 no.2
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• pp.183-190
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• 2015

Objective : Walking with a Material handling is an activity frequently undertaken by agricultural workers in Korea, due to the nature of their work. This study aimed to investigate differences in biomechanical variables according to the mechanical alignment of the lower limbs when walking with a heavy load, and to use this as basic data in the design of various working environments to reduce the skeletomuscular burden on the knee joint. Method : The study subjects comprised of 22 right-foot dominant adult men and women aged between 20 and 23 years. The subjects were divided into a varus or valgus group according to the mechanical alignment of the lower limb by using radiographic findings. The subjects walked without any load and with a load of 10%, 20%, or 30% of their body weight held in front of them. The Kwon3d XP program was used to calculate biomechanical variables. Results : The flexion/extension moment of the knee joint showed a decreasing trend with increased load, irrespective of the mechanical alignment of the lower limb, while the varus group did not show normal compensatory action when supported by one leg at the point of maximum vertical ground reaction force. In addition, in terms of the time taken, subjects showed no difficulties in one-foot support time up to 20%/BW, but at 30%/BW, despite individual differences, there was an increase in single limb. The increased load resulted in a decrease in the ratio of standing phase to ensure physical stability. The valgus group showed a trend of increasing the stability of their center of mass with increasing load, through higher braking power in the early standing phase. Conclusion : In conclusion, although there was no statistical difference in biomechanical variables according to the mechanical alignment of the lower limbs, the varus group showed a more irregular walking pattern with a Material handling than the valgus group, partially proving the association between lower limb alignment and walking with a Material handling.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.4
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• pp.19-28
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• 1999

In order to control thermal deformation of the machine origin of machine tools a empirical model and a compensation system have been developed, Prior to empirical modeling the volumetric error considering shape errors and joint errors of slides is formulated through the homogeneous transformation matrix (HTM) and kinematic chain. Simulation results of the HTM method show that the thermal error of the machine origin is more critical than position-dependent errors. In order to make a stable and effective software error compensation system the GMDH (Group Method of Data Handling) models are constructed to estimate the thermal deformation of the machine origin by measuring deformation data and temperature data. A test bar and gap sensors are used to measure the deformation data. In order to compensate the estimated error the work origin shift method is developed by implementing a digital I/O interface board between a CNC controller and an IBM PC. The method shifts the work origin as much as the amounts which are calculated by the pre-established thermal error model. The experiment results for a vertical machining center show that the thermal deformation of the machine origin is reduced within $\pm$5$mu extrm{m}$.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.473-473
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• 2011

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily reference evapotranspiration (ETo) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it consists of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily ETo data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as ETo modeling can be generalized using GMDH-NNM.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.791-793
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• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.217-222
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• 2016

Recent research on integrated and peer-to-peer databases has produced new methods for handling various types of shared-group and process data. This paper with data publishing, where the publisher needs to specify certain sensitive information that should be protected. The proposed method cannot infer the user's sensitive information is leaked by XML constraints. In addition, the proposed secure framework uses encrypt to prevent the leakage of sensitive information from authorized users. In this framework, each node of sensitive data in an eXtensible Markup Language (XML) document is encrypted separately. All of the encrypted data are moved from their original document, and are bundled with an encrypted structure index. Our experiments show that the proposed framework prevents information being leaked via data inference.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.827-829
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• 1999

The identification of nonlinear system using Extended GMDH(EGMDH) is studied in this paper. The proposed EGMDH algorithm is based on GMDH(Group Method of Data handling) method and its structure is similar to Neural Networks. The each node of EGMDH structure utilizes several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. As the operating condition changes, the parameters of EGMDH will also change, so the proposed scheme by means of the EGMDH method is capable of adapting rapidly to the changing environment. The simulation result shows that the simple nonlinear process can be modeled reasonably well by the proposed method which are simple but efficient.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1044-1047
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• 1996

In this paper, GMDH(Group Method of Data Handling) algorithm, which is based on heuristic self organization to predict and identify the complex system, is applied to the control system of DC servo motor. The mathematical relation between input voltage and motor speed is obtained by GMDH algorithm. A design method of model following control system based on GMDH algorithm is developed. As a result of applying this method to DC servo motor, the simulation and experiment have shown that the developed method gives a good performance in tracking the reference model and in rejection of disturbance, in spite of constant load and changing load.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.83-86
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• 1998

In this paper, a fuzzy model based on the Polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. the new algorithm uses PNN algorithm based on Group Mehtod of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.